A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power
Abstract
:1. Introduction
- (1)
- A two-stage multi-segment boundary approximation method (TSM) is proposed to describe the accurate feasible region of tie line power in an AC/DC hybrid distribution network. In the first stage of the TSM method, the linear constraints of the AC/DC hybrid distribution network are iteratively solved to obtain an approximate feasible region. In the second stage, the approximate feasible region of the first stage is modified by nonlinear constraints, and finally, the high-precision approximation of the real feasible region is obtained. Examples demonstrate how the suggested method’s accuracy and speed are significantly better than those of previous approaches.
- (2)
- A non-iterative coordinated optimization method based on feasible region projection is proposed. Aiming at the problem that the feasible region of tie line transmission power cannot retain cost information, a convolutional neural network (CNN) cost function fitting method is proposed. Through Monte Carlo sampling, the functional relationship between tie line transmission power and operating cost is obtained. Under the premise of protecting information privacy, alternating iteration is avoided, and high-precision coordinated operation of the AC/DC hybrid distribution network system is realized.
2. Materials and Methods
2.1. AC Network Modeling
2.1.1. AC Power Flow Equation
2.1.2. AC Distribution Network Security Constraints
2.2. DC Network Modeling
2.2.1. DC Power Flow Equation
2.2.2. DC Distribution Network Security Constraints
2.3. Converter Mathematical Model
3. Method of Characterization of the Feasible Region of the Tie Line
3.1. Search Process of Multi-Segment Boundary Approximation Method
- (1)
- Initialization of Boundary Points
- (2)
- Iteratively updating the approximation polytope
- (3)
- Accuracy Judgement of Multipoint Approximation
3.2. Two-Stage Multi-Segment Boundary Approximation Method (TSM)
Algorithm 1: Two-stage multi-segment (TSM) |
- (1)
- First-stage fast approximation. In the first stage, linearized grid constraints are used to quickly and iteratively search for boundary points of the feasible region. This can greatly improve computational efficiency and quickly obtain a rough approximation of the feasible region. The hybrid grid linear model is as follows:
- (i)
- AC linearized branch power flow model
- (ii)
- DC linearized branch power flow model
- (iii)
- VSC linearized model
- (2)
- The second stage of precise correction. The rough feasible region obtained in the first stage may have certain errors. The second stage uses precise nonlinear grid constraints to correct the boundary points obtained in the first stage and modify inaccurate boundary points. Finally, a high-precision feasible region approximation is obtained. Two-stage collaborative work not only ensures accuracy, but also greatly improves the convergence speed.
4. Artificial Intelligence-Based Scheduling Framework for Interconnected Systems
4.1. Convolutional Neural Network Model
4.2. Cost Fitting of Feasible Region of Tie Line in AC/DC Hybrid Power Grid Based on Convolutional Neural Network (CNN)
5. Case Studies
5.1. Simulation Setup and Comparison Methods
5.2. CNN-Based Cost Function Fitting
5.3. A Paradigm for Dispatching Interconnected Systems That Take into Account the Practical Range of AC/DC Hybrid Power Grid Transmission
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Method | CR | ER | Time (s) | Volume (MW2) |
---|---|---|---|---|
M0 | - | - | 87,165.3 | 0.5382 |
M1 | 18.21% | 0% | 2.44 | 0.0979 |
M2 | 99.86% | 0.01% | 4.63 | 0.5375 |
Number of Layers | Type | Structure and Parameters |
---|---|---|
First layer | Input layer | Enter the tie line power, and the number of input parameters is 6 |
Second layer | Conv layer | 16 convolution kernels, convolution sum size 3 × 1 |
Batchnorm layer | Batch normalization | |
ReLU layer | The kernel in the network layer uses L2 regularization | |
Maxpool layer | Pool size 2 × 1, stride 2 | |
Third floor | Conv layer | 32 convolution kernels, convolution sum size 3 × 1 |
Batchnorm layer | Batch normalization | |
ReLU layer | The kernel in the network layer uses L2 regularization | |
Maxpool layer | Pool size 2 × 1, stride 2 | |
Fourth floor | Conv layer | 64 convolution kernels, convolution sum size 3 × 1 |
Batchnorm layer | Batch normalization | |
ReLU layer | The kernel in the network layer uses L2 regularization | |
Maxpool layer | Pool size 2 × 1, stride 2 | |
Fifth floor | FullyConnected layer | 128 neurons fully connected layer |
Sixth floor | FullyConnected layer | 9 neuron fully connected layers |
Seventh floor | Output layer | Output fitting results |
Number of layers | Type | Structure and parameters |
Method | RMSE | MAPE | R2 |
---|---|---|---|
M0 | 1.2584 | 0.0142 | 0.8173 |
M1 | 0.9129 | 0.0090 | 0.9649 |
Scheduling Solution Method | Tie Line Transmission Power (p.u.) | ||
---|---|---|---|
PB1 | PB2 | PB3 | |
M0 | 0.712 | 0.062 | 0.124 |
M1 | 0.694 | 0.058 | 0.153 |
M2 | 0.709 | 0.058 | 0.104 |
M3 | 0.710 | 0.065 | 0.125 |
Scheduling Solution Method | Area 1 | Area 2 | Total Time | Total Generation Cost | ||
---|---|---|---|---|---|---|
Cost | Time | Cost | Time | |||
($) | (s) | ($) | (s) | (s) | ($) | |
M0 | 1.05 × 104 | - | 2.52 × 104 | - | 3.0741 | 2.3178 × 104 |
M1 | 1.15 × 104 | 71.353 | 2.58 × 104 | 18.253 | 86.605 | 2.5225 × 104 |
M2 | 1.13 × 104 | 62.534 | 2.48 × 104 | 15.302 | 77.832 | 2.4835 × 104 |
M3 | 1.03 × 104 | 22.357 | 2.48 × 104 | 10.724 | 33.081 | 2.3182 × 104 |
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Dai, W.; Gao, Y.; Goh, H.H.; Jian, J.; Zeng, Z.; Liu, Y. A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power. Energies 2024, 17, 1462. https://doi.org/10.3390/en17061462
Dai W, Gao Y, Goh HH, Jian J, Zeng Z, Liu Y. A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power. Energies. 2024; 17(6):1462. https://doi.org/10.3390/en17061462
Chicago/Turabian StyleDai, Wei, Yang Gao, Hui Hwang Goh, Jiangyi Jian, Zhihong Zeng, and Yuelin Liu. 2024. "A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power" Energies 17, no. 6: 1462. https://doi.org/10.3390/en17061462
APA StyleDai, W., Gao, Y., Goh, H. H., Jian, J., Zeng, Z., & Liu, Y. (2024). A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power. Energies, 17(6), 1462. https://doi.org/10.3390/en17061462